Data Architect vs. Data Engineer: How to Choose the Right Career in Data

Key takeaways

Data architects provide the blueprint for an organization’s data infrastructure, which data engineers use to build the enterprise’s data architecture.

• Since it’s a senior-level role, data architects, who earn a median salary of $178,000 annually, tend to earn higher salaries than data engineers, who earn a median salary of $132,000 annually [1,2]. 

• A data architect’s skills focus on data modeling and database design, while a data engineer typically needs to understand how to organize and build data systems and data pipelines.

• You can become a data architect by starting out your career as a data engineer and accumulating several years of experience.

Discover how data architect and data engineer roles compare based on their job responsibilities, required education and skills, and earning potential. If you’re ready to start building expertise in data engineering, enroll in the IBM Data Engineering Professional Certificate. In as little as six months, you’ll have the opportunity to gain experience with ETL processes, data warehousing, database administration, and other concepts that can benefit your career as a data engineer or even a data architect. Upon completion, you’ll have earned a career certificate for your resume.

Data architect vs. data engineer: Key responsibilities and daily tasks

A data architect designs data management and storage solutions for an organization, while data engineers implement a data architect’s plan by building the systems that facilitate efficient data flow through the data pipeline. A data architect’s role is more strategic, focusing on the optimal storage and access of large amounts of data for a business. In contrast, a data engineer’s role is on the operational and practical side, constructing and maintaining the infrastructure specified in the data architect’s blueprint.

Other responsibilities of data architects include:

• Translating business requirements into integrations, databases, data streams, and data warehouses

• Defining the framework for data architecture and governance standards to ensure data consistency and compliance

• Collaborating with executives, stakeholders, and other departments to decide on the best data management strategy

• Developing patterns for data engineers and other professionals to follow to improve data analytics and systems

Meanwhile, other key responsibilities of data engineers include:

• Developing and managing extract, transform, load (ETL) pipelines to integrate massive volumes of data from multiple sources 

• Developing, maintaining, and monitoring databases, data warehouses, and integration tools

• Implementing automation, algorithms, and custom scripts for data analysis, processing, and management

• Monitoring and resolving data pipeline bottlenecks, bugs, or downtimes

Educational requirements and certifications for data engineers vs. data architects

Both data engineers and data architects may begin with a bachelor’s degree in computer science, information technology, software engineering, or data science. Data architects may focus on coursework involving data modeling, enterprise architecture, systems development, and information management, while data engineers often take courses in algebra and statistics, database management, computer programming, and system analysis. 

Advanced education

Pursuing higher education, like a master’s degree, is often required for data architect roles to develop a more comprehensive understanding of data environments and design effective data solutions that meet business needs. Data architects may pursue a master’s degree in information systems, big data analytics, data science, and systems engineering, or even business information systems, data or business management, or business architecture to gain business-focused strategic planning skills.

On the other hand, data engineers may choose to reinforce their technical and data analysis skills by pursuing a master’s degree in a computer science or data science-related field that includes coursework in cloud computing, big data, and machine learning.

Certifications

You can find popular data engineer certifications that validate skills in areas such as cloud platforms, tools and processes, or fundamental data engineering concepts. Consider the following certifications for data engineers as well as their focus areas: 

• Cloud platforms: Google Cloud Professional Data Engineer, AWS Certified Data Engineer - Associate 

• Tools and processes: Databricks Certified Data Engineer Professional, SnowPro Core, Cloudera Certified Professional (CCP) Data Engineer

• Foundational concepts: Data Science Council of America (DASCA) Associate Big Data Engineer

Data architect certifications typically test your ability to design data and cloud solutions or validate your understanding of data management and enterprise architecture concepts. If you’re interested in validating your skills as a data architect, one of the following certifications may be a good option for you:

• Data and cloud solutions design: Microsoft Certified: Azure Solutions Architect Expert, AWS Certified Solutions Architect - Associate

• Data management and enterprise architecture: DAMA International Certified Data Management Professionals, TOGAF certifications

Can a data engineer become a data architect?

Yes, many data architects start off in data engineer roles and work towards a data architect role by building years of experience in data modeling, data management, systems design, database architecture, etc. 

Skills required for a data architect vs. a data engineer

A data architect’s skills focus on data modeling and database design, while a data engineer needs to understand how to organize and build data systems and data pipelines. Data architects and data engineers may have an overlapping skill set in data technologies, but how they use these in data management may differ. For example, both data architects and data engineers need to understand cloud platforms such as AWS, Google Cloud, or Microsoft Azure. However, as a data engineer, you will need to know how to leverage cloud technologies to build scalable data systems, while data architects need this skill to plan for expansive data reservoir architectures.

Both data architects and data engineers need to understand databases, data lakes, data warehouses, and ETL processes. In some cases, data architects will have the same skills as data engineers but with more specialized knowledge. For example, data architects need to have an in-depth understanding of data security protocols and governance policies, while data engineers need to know how to follow security procedures to ensure data safety. While a strong command of programming languages like Python, SQL, Java, and Scala is crucial for data engineers to write code for data transformation and pipeline automation, programming skills may not always be mandatory for data architects, although some understanding may be necessary to develop effective data solutions.

In terms of workplace skills, data architects need strategic thinking to plan data architectures that align with business objectives and effective communication to convey technical concepts to nontechnical stakeholders. For data engineers, problem-solving is a key skill for effectively identifying and fixing issues and improving system performance.

Data engineer vs. data architect salary

Since a data architect is a more senior role than a data engineer, they tend to earn higher salaries due to their expertise in strategic planning and ensuring compliance. Though data engineers also tend to earn competitive salaries, which may vary by experience and company. The table below compares the median total pay for data architects and data engineers in the US, as well as how their salaries change with years of experience. 

The salary information below is the median total pay from Glassdoor as of February 2026. These figures include both base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other forms of compensation.

What is the difference between data architecture and data engineering growth opportunities and career paths? 

Both data architects and data engineers will experience significant demand over the next few years. Global data traffic doubled between 2020 and 2025, and is expected to continue growing at this fast pace [3]. With the rapid increase in data volume, companies will likely continue to need professionals, such as data architects and data engineers, who can plan how to organize their data and build the infrastructure for their data analytics. 

As a data engineer, you may specialize in certain fields to work as a big data engineer, cloud data engineer, data integration engineer, or AI/ML engineer. The typical career path of a data engineer is as follows:

• Entry-level: You might start your career as a junior data engineer or data analyst, gaining skills in pipeline construction, ETL processes, and data manipulation. 

• Mid-level: After gaining two to five years of experience, you may be ready to move into a mid-level position like data architect, data scientist, solutions architect, or ETL developer. Your role at this point can involve real-time data processing, working with complex pipelines, mastering cloud computing and data architectures, and developing tailored business solutions. 

• Senior-level: Once you’ve accrued five to eight years of experience as a data engineer, more senior-level roles like senior data engineer or data engineering manager may become available to you. In these roles, you might lead teams of data professionals and develop an organization’s data strategy.

While data engineering is one pathway to a data architecture role, several other jobs can help you transition into a data architecture role. Discover the general pathway for data architects below.

• Entry-level: You can build your experience for a data architect job through positions like data modeler, data warehouse developer, SQL developer, data engineer, or other roles that focus on enterprise architecture. 

• Mid-level: Once you’ve gained three to seven years of experience, you can transition to a mid-level role like solutions architect or database administrator. After you’ve accumulated around nine years of experience, you can explore a data architect role where you may lead the data strategy of an enterprise and ensure data architectures support the organization’s operational and analytical goals. 

• Senior-level: With over 10 years of experience, you can progress to roles like senior data architect, chief data officer, enterprise project manager, or information technology director, overseeing multiple data projects, leading teams of data architects, and spearheading an organization’s digital transformation efforts.

Data scientist vs. data engineer vs. data architect

The data scientist, data engineer, and data architect roles are all interlinked and form an important part of a data science team. The data architect designs and manages a company’s data architecture and provides a framework for data storage and management. The data engineer uses this framework to develop and maintain an accessible and well-structured data infrastructure and data pipeline. Data scientists use the data stored in the infrastructure following the processes developed by the data engineer to analyze and extract meaning from the data.

Read more: Data Engineer vs. Data Scientist: What’s the Difference?

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